[{"file_date_updated":"2019-07-16T06:08:31Z","month":"07","year":"2019","file":[{"file_size":815416,"open_access":1,"date_created":"2019-07-16T06:08:31Z","file_name":"2019_Evolution_TrubenovaBarbora.pdf","creator":"apreinsp","access_level":"open_access","success":1,"relation":"main_file","date_updated":"2019-07-16T06:08:31Z","file_id":"6643","content_type":"application/pdf"}],"ddc":["576"],"title":"Surfing on the seascape: Adaptation in a changing environment","language":[{"iso":"eng"}],"project":[{"_id":"25AEDD42-B435-11E9-9278-68D0E5697425","grant_number":"704172","name":"Rate of Adaptation in Changing Environment"},{"name":"Speed of Adaptation in Population Genetics and Evolutionary Computation","grant_number":"618091","_id":"25B1EC9E-B435-11E9-9278-68D0E5697425"}],"citation":{"ama":"Trubenova B, Krejca M, Lehre PK, Kötzing T. Surfing on the seascape: Adaptation in a changing environment. Evolution. 2019;73(7):1356-1374. doi:10.1111/evo.13784","mla":"Trubenova, Barbora, et al. “Surfing on the Seascape: Adaptation in a Changing Environment.” Evolution, vol. 73, no. 7, Wiley, 2019, pp. 1356–74, doi:10.1111/evo.13784.","chicago":"Trubenova, Barbora, Martin Krejca, Per Kristian Lehre, and Timo Kötzing. “Surfing on the Seascape: Adaptation in a Changing Environment.” Evolution 73, no. 7 (2019): 1356–74. https://doi.org/10.1111/evo.13784.","apa":"Trubenova, B., Krejca, M., Lehre, P. K., & Kötzing, T. (2019). Surfing on the seascape: Adaptation in a changing environment. Evolution, 73(7), 1356–1374. https://doi.org/10.1111/evo.13784","ista":"Trubenova B, Krejca M, Lehre PK, Kötzing T. 2019. Surfing on the seascape: Adaptation in a changing environment. Evolution. 73(7), 1356–1374.","ieee":"B. Trubenova, M. Krejca, P. K. Lehre, and T. Kötzing, “Surfing on the seascape: Adaptation in a changing environment,” Evolution, vol. 73, no. 7, pp. 1356–1374, 2019.","short":"B. Trubenova, M. Krejca, P.K. Lehre, T. Kötzing, Evolution 73 (2019) 1356–1374."},"intvolume":" 73","day":"01","accept":"1","cc_license":"cc_by_nc_nd","doi":"10.1111/evo.13784","user_id":"4435EBFC-F248-11E8-B48F-1D18A9856A87","oa":1,"acknowledgement":"The authors would like to thank to Tiago Paixao and Nick Barton for useful comments and advice.","quality_controlled":"1","date_updated":"2019-08-02T12:39:23Z","author":[{"full_name":"Trubenova, Barbora","first_name":"Barbora","id":"42302D54-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-6873-2967","last_name":"Trubenova"},{"first_name":"Martin ","full_name":"Krejca, Martin ","last_name":"Krejca"},{"last_name":"Lehre","full_name":"Lehre, Per Kristian","first_name":"Per Kristian"},{"last_name":"Kötzing","first_name":"Timo","full_name":"Kötzing, Timo"}],"abstract":[{"text":"The environment changes constantly at various time scales and, in order to survive, species need to keep adapting. Whether these species succeed in avoiding extinction is a major evolutionary question. Using a multilocus evolutionary model of a mutation‐limited population adapting under strong selection, we investigate the effects of the frequency of environmental fluctuations on adaptation. Our results rely on an “adaptive‐walk” approximation and use mathematical methods from evolutionary computation theory to investigate the interplay between fluctuation frequency, the similarity of environments, and the number of loci contributing to adaptation. First, we assume a linear additive fitness function, but later generalize our results to include several types of epistasis. We show that frequent environmental changes prevent populations from reaching a fitness peak, but they may also prevent the large fitness loss that occurs after a single environmental change. Thus, the population can survive, although not thrive, in a wide range of conditions. Furthermore, we show that in a frequently changing environment, the similarity of threats that a population faces affects the level of adaptation that it is able to achieve. We check and supplement our analytical results with simulations.","lang":"eng"}],"status":"public","type":"journal_article","department":[{"_id":"NiBa"}],"date_created":"2019-07-14T21:59:20Z","publication":"Evolution","_id":"6637","publisher":"Wiley","page":"1356-1374","publication_status":"published","issue":"7","date_published":"2019-07-01T00:00:00Z","article_type":"original","oa_version":"Published Version","volume":73},{"oa_version":"Published Version","article_type":"original","publication_identifier":{"eissn":["20457758"]},"publication_status":"epub_ahead","date_published":"2019-07-02T00:00:00Z","_id":"6795","publication":"Ecology and Evolution","publisher":"Wiley","article_processing_charge":"No","date_created":"2019-08-11T21:59:24Z","department":[{"_id":"NiBa"}],"author":[{"orcid":"0000-0002-6873-2967","last_name":"Trubenova","id":"42302D54-F248-11E8-B48F-1D18A9856A87","full_name":"Trubenova, Barbora","first_name":"Barbora"},{"last_name":"Hager","full_name":"Hager, Reinmar","first_name":"Reinmar"}],"quality_controlled":"1","date_updated":"2019-08-12T07:36:03Z","status":"public","type":"journal_article","abstract":[{"lang":"eng","text":"The green‐beard effect is one proposed mechanism predicted to underpin the evolu‐tion of altruistic behavior. It relies on the recognition and the selective help of altruists to each other in order to promote and sustain altruistic behavior. However, this mechanism has often been dismissed as unlikely or uncommon, as it is assumed that both the signaling trait and altruistic trait need to be encoded by the same gene or through tightly linked genes. Here, we use models of indirect genetic effects (IGEs) to find the minimum correlation between the signaling and altruistic trait required for the evolution of the latter. We show that this correlation threshold depends on the strength of the interaction (influence of the green beard on the expression of the altruistic trait), as well as the costs and benefits of the altruistic behavior. We further show that this correlation does not necessarily have to be high and support our analytical results by simulations."}],"cc_license":"'https://creativecommons.org/licenses/by/4.0/'","doi":"10.1002/ece3.5484","oa":1,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","ddc":["576"],"file":[{"creator":"dernst","success":1,"access_level":"open_access","relation":"main_file","content_type":"application/pdf","file_id":"6799","date_updated":"2019-08-12T07:30:30Z","file_size":2839636,"file_name":"2019_EcologyEvolution_Trubenova.pdf","open_access":1,"date_created":"2019-08-12T07:30:30Z"}],"year":"2019","title":"Green beards in the light of indirect genetic effects","day":"02","accept":"1","language":[{"iso":"eng"}],"citation":{"mla":"Trubenova, Barbora, and Reinmar Hager. “Green Beards in the Light of Indirect Genetic Effects.” Ecology and Evolution, Wiley, 2019, doi:10.1002/ece3.5484.","ama":"Trubenova B, Hager R. Green beards in the light of indirect genetic effects. Ecology and Evolution. 2019. doi:10.1002/ece3.5484","chicago":"Trubenova, Barbora, and Reinmar Hager. “Green Beards in the Light of Indirect Genetic Effects.” Ecology and Evolution, 2019. https://doi.org/10.1002/ece3.5484.","apa":"Trubenova, B., & Hager, R. (2019). Green beards in the light of indirect genetic effects. Ecology and Evolution. https://doi.org/10.1002/ece3.5484","short":"B. Trubenova, R. Hager, Ecology and Evolution (2019).","ista":"Trubenova B, Hager R. 2019. Green beards in the light of indirect genetic effects. Ecology and Evolution.","ieee":"B. Trubenova and R. Hager, “Green beards in the light of indirect genetic effects,” Ecology and Evolution, 2019."},"project":[{"grant_number":"704172","name":"Rate of Adaptation in Changing Environment","_id":"25AEDD42-B435-11E9-9278-68D0E5697425"}],"file_date_updated":"2019-08-12T07:30:30Z","month":"07"},{"year":"2018","file":[{"date_updated":"2018-12-12T10:08:14Z","content_type":"application/pdf","file_id":"4674","relation":"main_file","access_level":"open_access","creator":"system","date_created":"2018-12-12T10:08:14Z","open_access":1,"file_name":"IST-2018-1014-v1+1_2018_Paixao_Escape.pdf","file_size":691245}],"ddc":["576"],"title":"How to escape local optima in black box optimisation when non elitism outperforms elitism","accept":"1","day":"01","intvolume":" 80","citation":{"short":"P. Oliveto, T. Paixao, J. Pérez Heredia, D. Sudholt, B. Trubenova, Algorithmica 80 (2018) 1604–1633.","ista":"Oliveto P, Paixao T, Pérez Heredia J, Sudholt D, Trubenova B. 2018. How to escape local optima in black box optimisation when non elitism outperforms elitism. Algorithmica. 80(5), 1604–1633.","ieee":"P. Oliveto, T. Paixao, J. Pérez Heredia, D. Sudholt, and B. Trubenova, “How to escape local optima in black box optimisation when non elitism outperforms elitism,” Algorithmica, vol. 80, no. 5, pp. 1604–1633, 2018.","apa":"Oliveto, P., Paixao, T., Pérez Heredia, J., Sudholt, D., & Trubenova, B. (2018). How to escape local optima in black box optimisation when non elitism outperforms elitism. Algorithmica, 80(5), 1604–1633. https://doi.org/10.1007/s00453-017-0369-2","chicago":"Oliveto, Pietro, Tiago Paixao, Jorge Pérez Heredia, Dirk Sudholt, and Barbora Trubenova. “How to Escape Local Optima in Black Box Optimisation When Non Elitism Outperforms Elitism.” Algorithmica 80, no. 5 (2018): 1604–33. https://doi.org/10.1007/s00453-017-0369-2.","mla":"Oliveto, Pietro, et al. “How to Escape Local Optima in Black Box Optimisation When Non Elitism Outperforms Elitism.” Algorithmica, vol. 80, no. 5, Springer, 2018, pp. 1604–33, doi:10.1007/s00453-017-0369-2.","ama":"Oliveto P, Paixao T, Pérez Heredia J, Sudholt D, Trubenova B. How to escape local optima in black box optimisation when non elitism outperforms elitism. Algorithmica. 2018;80(5):1604-1633. doi:10.1007/s00453-017-0369-2"},"project":[{"grant_number":"618091","name":"Speed of Adaptation in Population Genetics and Evolutionary Computation","_id":"25B1EC9E-B435-11E9-9278-68D0E5697425"}],"language":[{"iso":"eng"}],"file_date_updated":"2018-12-12T10:08:14Z","month":"05","author":[{"last_name":"Oliveto","first_name":"Pietro","full_name":"Oliveto, Pietro"},{"last_name":"Paixao","orcid":"0000-0003-2361-3953","first_name":"Tiago","full_name":"Paixao, Tiago","id":"2C5658E6-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Pérez Heredia","full_name":"Pérez Heredia, Jorge","first_name":"Jorge"},{"full_name":"Sudholt, Dirk","first_name":"Dirk","last_name":"Sudholt"},{"full_name":"Trubenova, Barbora","first_name":"Barbora","id":"42302D54-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-6873-2967","last_name":"Trubenova"}],"date_updated":"2019-08-02T12:39:28Z","quality_controlled":"1","status":"public","type":"journal_article","abstract":[{"text":"Escaping local optima is one of the major obstacles to function optimisation. Using the metaphor of a fitness landscape, local optima correspond to hills separated by fitness valleys that have to be overcome. We define a class of fitness valleys of tunable difficulty by considering their length, representing the Hamming path between the two optima and their depth, the drop in fitness. For this function class we present a runtime comparison between stochastic search algorithms using different search strategies. The (1+1) EA is a simple and well-studied evolutionary algorithm that has to jump across the valley to a point of higher fitness because it does not accept worsening moves (elitism). In contrast, the Metropolis algorithm and the Strong Selection Weak Mutation (SSWM) algorithm, a famous process in population genetics, are both able to cross the fitness valley by accepting worsening moves. We show that the runtime of the (1+1) EA depends critically on the length of the valley while the runtimes of the non-elitist algorithms depend crucially on the depth of the valley. Moreover, we show that both SSWM and Metropolis can also efficiently optimise a rugged function consisting of consecutive valleys.","lang":"eng"}],"cc_license":"'https://creativecommons.org/licenses/by/4.0/'","doi":"10.1007/s00453-017-0369-2","pubrep_id":"1014","publist_id":"6957","oa":1,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publication":"Algorithmica","_id":"723","publisher":"Springer","date_created":"2018-12-11T11:48:09Z","department":[{"_id":"NiBa"},{"_id":"CaGu"}],"oa_version":"Published Version","volume":80,"publication_status":"published","page":"1604 - 1633","date_published":"2018-05-01T00:00:00Z","issue":"5"},{"quality_controlled":"1","date_updated":"2019-08-02T12:36:48Z","oa_version":"None","author":[{"full_name":"Heredia, Jorge","first_name":"Jorge","last_name":"Heredia"},{"last_name":"Trubenova","orcid":"0000-0002-6873-2967","id":"42302D54-F248-11E8-B48F-1D18A9856A87","first_name":"Barbora","full_name":"Trubenova, Barbora"},{"full_name":"Sudholt, Dirk","first_name":"Dirk","last_name":"Sudholt"},{"full_name":"Paixao, Tiago","first_name":"Tiago","id":"2C5658E6-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0003-2361-3953","last_name":"Paixao"}],"abstract":[{"text":"Adaptation depends critically on the effects of new mutations and their dependency on the genetic background in which they occur. These two factors can be summarized by the fitness landscape. However, it would require testing all mutations in all backgrounds, making the definition and analysis of fitness landscapes mostly inaccessible. Instead of postulating a particular fitness landscape, we address this problem by considering general classes of landscapes and calculating an upper limit for the time it takes for a population to reach a fitness peak, circumventing the need to have full knowledge about the fitness landscape. We analyze populations in the weak-mutation regime and characterize the conditions that enable them to quickly reach the fitness peak as a function of the number of sites under selection. We show that for additive landscapes there is a critical selection strength enabling populations to reach high-fitness genotypes, regardless of the distribution of effects. This threshold scales with the number of sites under selection, effectively setting a limit to adaptation, and results from the inevitable increase in deleterious mutational pressure as the population adapts in a space of discrete genotypes. Furthermore, we show that for the class of all unimodal landscapes this condition is sufficient but not necessary for rapid adaptation, as in some highly epistatic landscapes the critical strength does not depend on the number of sites under selection; effectively removing this barrier to adaptation.","lang":"eng"}],"volume":205,"publication_identifier":{"issn":["00166731"]},"type":"journal_article","status":"public","page":"803 - 825","publication_status":"published","doi":"10.1534/genetics.116.189340","user_id":"4435EBFC-F248-11E8-B48F-1D18A9856A87","issue":"2","publist_id":"6256","date_published":"2017-02-01T00:00:00Z","publication":"Genetics","_id":"1111","year":"2017","title":"Selection limits to adaptive walks on correlated landscapes","language":[{"iso":"eng"}],"intvolume":" 205","citation":{"apa":"Heredia, J., Trubenova, B., Sudholt, D., & Paixao, T. (2017). Selection limits to adaptive walks on correlated landscapes. Genetics, 205(2), 803–825. https://doi.org/10.1534/genetics.116.189340","ieee":"J. Heredia, B. Trubenova, D. Sudholt, and T. Paixao, “Selection limits to adaptive walks on correlated landscapes,” Genetics, vol. 205, no. 2, pp. 803–825, 2017.","ista":"Heredia J, Trubenova B, Sudholt D, Paixao T. 2017. Selection limits to adaptive walks on correlated landscapes. Genetics. 205(2), 803–825.","short":"J. Heredia, B. Trubenova, D. Sudholt, T. Paixao, Genetics 205 (2017) 803–825.","ama":"Heredia J, Trubenova B, Sudholt D, Paixao T. Selection limits to adaptive walks on correlated landscapes. Genetics. 2017;205(2):803-825. doi:10.1534/genetics.116.189340","mla":"Heredia, Jorge, et al. “Selection Limits to Adaptive Walks on Correlated Landscapes.” Genetics, vol. 205, no. 2, Genetics Society of America, 2017, pp. 803–25, doi:10.1534/genetics.116.189340.","chicago":"Heredia, Jorge, Barbora Trubenova, Dirk Sudholt, and Tiago Paixao. “Selection Limits to Adaptive Walks on Correlated Landscapes.” Genetics 205, no. 2 (2017): 803–25. https://doi.org/10.1534/genetics.116.189340."},"project":[{"_id":"25B1EC9E-B435-11E9-9278-68D0E5697425","grant_number":"618091","name":"Speed of Adaptation in Population Genetics and Evolutionary Computation"}],"day":"01","publisher":"Genetics Society of America","department":[{"_id":"NiBa"}],"date_created":"2018-12-11T11:50:12Z","month":"02"},{"citation":{"ieee":"T. Paixao, J. Pérez Heredia, D. Sudholt, and B. Trubenova, “Towards a runtime comparison of natural and artificial evolution,” Algorithmica, vol. 78, no. 2, pp. 681–713, 2017.","ista":"Paixao T, Pérez Heredia J, Sudholt D, Trubenova B. 2017. Towards a runtime comparison of natural and artificial evolution. Algorithmica. 78(2), 681–713.","short":"T. Paixao, J. Pérez Heredia, D. Sudholt, B. Trubenova, Algorithmica 78 (2017) 681–713.","apa":"Paixao, T., Pérez Heredia, J., Sudholt, D., & Trubenova, B. (2017). Towards a runtime comparison of natural and artificial evolution. Algorithmica, 78(2), 681–713. https://doi.org/10.1007/s00453-016-0212-1","chicago":"Paixao, Tiago, Jorge Pérez Heredia, Dirk Sudholt, and Barbora Trubenova. “Towards a Runtime Comparison of Natural and Artificial Evolution.” Algorithmica 78, no. 2 (2017): 681–713. https://doi.org/10.1007/s00453-016-0212-1.","ama":"Paixao T, Pérez Heredia J, Sudholt D, Trubenova B. Towards a runtime comparison of natural and artificial evolution. Algorithmica. 2017;78(2):681-713. doi:10.1007/s00453-016-0212-1","mla":"Paixao, Tiago, et al. “Towards a Runtime Comparison of Natural and Artificial Evolution.” Algorithmica, vol. 78, no. 2, Springer, 2017, pp. 681–713, doi:10.1007/s00453-016-0212-1."},"project":[{"_id":"25B1EC9E-B435-11E9-9278-68D0E5697425","name":"Speed of Adaptation in Population Genetics and Evolutionary Computation","grant_number":"618091"}],"intvolume":" 78","language":[{"iso":"eng"}],"accept":"1","day":"01","ddc":["576"],"year":"2017","file":[{"relation":"main_file","file_id":"4805","content_type":"application/pdf","date_updated":"2018-12-12T10:10:19Z","creator":"system","access_level":"open_access","file_name":"IST-2016-658-v1+1_s00453-016-0212-1.pdf","open_access":1,"date_created":"2018-12-12T10:10:19Z","file_size":710206}],"title":"Towards a runtime comparison of natural and artificial evolution","month":"06","file_date_updated":"2018-12-12T10:10:19Z","abstract":[{"text":"Evolutionary algorithms (EAs) form a popular optimisation paradigm inspired by natural evolution. In recent years the field of evolutionary computation has developed a rigorous analytical theory to analyse the runtimes of EAs on many illustrative problems. Here we apply this theory to a simple model of natural evolution. In the Strong Selection Weak Mutation (SSWM) evolutionary regime the time between occurrences of new mutations is much longer than the time it takes for a mutated genotype to take over the population. In this situation, the population only contains copies of one genotype and evolution can be modelled as a stochastic process evolving one genotype by means of mutation and selection between the resident and the mutated genotype. The probability of accepting the mutated genotype then depends on the change in fitness. We study this process, SSWM, from an algorithmic perspective, quantifying its expected optimisation time for various parameters and investigating differences to a similar evolutionary algorithm, the well-known (1+1) EA. We show that SSWM can have a moderate advantage over the (1+1) EA at crossing fitness valleys and study an example where SSWM outperforms the (1+1) EA by taking advantage of information on the fitness gradient.","lang":"eng"}],"status":"public","type":"journal_article","date_updated":"2019-08-02T12:37:00Z","quality_controlled":"1","author":[{"orcid":"0000-0003-2361-3953","last_name":"Paixao","full_name":"Paixao, Tiago","first_name":"Tiago","id":"2C5658E6-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Pérez Heredia","first_name":"Jorge","full_name":"Pérez Heredia, Jorge"},{"last_name":"Sudholt","full_name":"Sudholt, Dirk","first_name":"Dirk"},{"first_name":"Barbora","full_name":"Trubenova, Barbora","id":"42302D54-F248-11E8-B48F-1D18A9856A87","last_name":"Trubenova","orcid":"0000-0002-6873-2967"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa":1,"publist_id":"5931","cc_license":"'https://creativecommons.org/licenses/by/4.0/'","doi":"10.1007/s00453-016-0212-1","pubrep_id":"658","publisher":"Springer","publication":"Algorithmica","_id":"1336","department":[{"_id":"NiBa"},{"_id":"CaGu"}],"date_created":"2018-12-11T11:51:27Z","publication_identifier":{"issn":["01784617"]},"volume":78,"oa_version":"Published Version","issue":"2","date_published":"2017-06-01T00:00:00Z","page":"681 - 713","publication_status":"published"},{"month":"07","file_date_updated":"2018-12-12T10:16:27Z","day":"20","accept":"1","language":[{"iso":"eng"}],"project":[{"_id":"25B1EC9E-B435-11E9-9278-68D0E5697425","grant_number":"618091","name":"Speed of Adaptation in Population Genetics and Evolutionary Computation"}],"citation":{"ama":"Oliveto P, Paixao T, Heredia J, Sudholt D, Trubenova B. When non-elitism outperforms elitism for crossing fitness valleys. In: Proceedings of the Genetic and Evolutionary Computation Conference 2016 . ACM; 2016:1163-1170. doi:10.1145/2908812.2908909","mla":"Oliveto, Pietro, et al. “When Non-Elitism Outperforms Elitism for Crossing Fitness Valleys.” Proceedings of the Genetic and Evolutionary Computation Conference 2016 , ACM, 2016, pp. 1163–70, doi:10.1145/2908812.2908909.","chicago":"Oliveto, Pietro, Tiago Paixao, Jorge Heredia, Dirk Sudholt, and Barbora Trubenova. “When Non-Elitism Outperforms Elitism for Crossing Fitness Valleys.” In Proceedings of the Genetic and Evolutionary Computation Conference 2016 , 1163–70. ACM, 2016. https://doi.org/10.1145/2908812.2908909.","apa":"Oliveto, P., Paixao, T., Heredia, J., Sudholt, D., & Trubenova, B. (2016). When non-elitism outperforms elitism for crossing fitness valleys. In Proceedings of the Genetic and Evolutionary Computation Conference 2016 (pp. 1163–1170). Denver, CO, USA: ACM. https://doi.org/10.1145/2908812.2908909","ieee":"P. Oliveto, T. Paixao, J. Heredia, D. Sudholt, and B. Trubenova, “When non-elitism outperforms elitism for crossing fitness valleys,” in Proceedings of the Genetic and Evolutionary Computation Conference 2016 , Denver, CO, USA, 2016, pp. 1163–1170.","ista":"Oliveto P, Paixao T, Heredia J, Sudholt D, Trubenova B. 2016. When non-elitism outperforms elitism for crossing fitness valleys. Proceedings of the Genetic and Evolutionary Computation Conference 2016 . GECCO: Genetic and evolutionary computation conference 1163–1170.","short":"P. Oliveto, T. Paixao, J. Heredia, D. Sudholt, B. Trubenova, in:, Proceedings of the Genetic and Evolutionary Computation Conference 2016 , ACM, 2016, pp. 1163–1170."},"file":[{"file_size":979026,"date_created":"2018-12-12T10:16:27Z","open_access":1,"file_name":"IST-2016-650-v1+1_p1163-oliveto.pdf","creator":"system","access_level":"open_access","relation":"main_file","date_updated":"2018-12-12T10:16:27Z","content_type":"application/pdf","file_id":"5214"}],"year":"2016","ddc":["576"],"title":"When non-elitism outperforms elitism for crossing fitness valleys","oa":1,"publist_id":"5900","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","pubrep_id":"650","cc_license":"'https://creativecommons.org/licenses/by/4.0/'","doi":"10.1145/2908812.2908909","type":"conference","status":"public","abstract":[{"text":"Crossing fitness valleys is one of the major obstacles to function optimization. In this paper we investigate how the structure of the fitness valley, namely its depth d and length ℓ, influence the runtime of different strategies for crossing these valleys. We present a runtime comparison between the (1+1) EA and two non-elitist nature-inspired algorithms, Strong Selection Weak Mutation (SSWM) and the Metropolis algorithm. While the (1+1) EA has to jump across the valley to a point of higher fitness because it does not accept decreasing moves, the non-elitist algorithms may cross the valley by accepting worsening moves. We show that while the runtime of the (1+1) EA algorithm depends critically on the length of the valley, the runtimes of the non-elitist algorithms depend crucially only on the depth of the valley. In particular, the expected runtime of both SSWM and Metropolis is polynomial in ℓ and exponential in d while the (1+1) EA is efficient only for valleys of small length. Moreover, we show that both SSWM and Metropolis can also efficiently optimize a rugged function consisting of consecutive valleys.","lang":"eng"}],"author":[{"first_name":"Pietro","full_name":"Oliveto, Pietro","last_name":"Oliveto"},{"orcid":"0000-0003-2361-3953","last_name":"Paixao","full_name":"Paixao, Tiago","first_name":"Tiago","id":"2C5658E6-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Heredia","first_name":"Jorge","full_name":"Heredia, Jorge"},{"last_name":"Sudholt","full_name":"Sudholt, Dirk","first_name":"Dirk"},{"last_name":"Trubenova","orcid":"0000-0002-6873-2967","first_name":"Barbora","full_name":"Trubenova, Barbora","id":"42302D54-F248-11E8-B48F-1D18A9856A87"}],"quality_controlled":"1","date_updated":"2019-08-02T12:37:01Z","date_created":"2018-12-11T11:51:31Z","department":[{"_id":"NiBa"},{"_id":"CaGu"}],"conference":{"name":"GECCO: Genetic and evolutionary computation conference","end_date":"2016-07-24","location":"Denver, CO, USA","start_date":"2016-07-20"},"publisher":"ACM","publication":"Proceedings of the Genetic and Evolutionary Computation Conference 2016 ","_id":"1349","date_published":"2016-07-20T00:00:00Z","publication_status":"published","page":"1163 - 1170","oa_version":"Published Version"},{"publisher":"Elsevier","_id":"1542","publication":" Journal of Theoretical Biology","department":[{"_id":"NiBa"},{"_id":"CaGu"}],"date_created":"2018-12-11T11:52:37Z","volume":383,"oa_version":"Published Version","date_published":"2015-10-21T00:00:00Z","page":"28 - 43","publication_status":"published","project":[{"name":"Speed of Adaptation in Population Genetics and Evolutionary Computation","grant_number":"618091","_id":"25B1EC9E-B435-11E9-9278-68D0E5697425"},{"grant_number":"250152","name":"Limits to selection in biology and in evolutionary computation","_id":"25B07788-B435-11E9-9278-68D0E5697425"}],"intvolume":" 383","citation":{"chicago":"Paixao, Tiago, Golnaz Badkobeh, Nicholas H Barton, Doğan Çörüş, Duccuong Dang, Tobias Friedrich, Per Lehre, Dirk Sudholt, Andrew Sutton, and Barbora Trubenova. “Toward a Unifying Framework for Evolutionary Processes.” Journal of Theoretical Biology 383 (2015): 28–43. https://doi.org/10.1016/j.jtbi.2015.07.011.","mla":"Paixao, Tiago, et al. “Toward a Unifying Framework for Evolutionary Processes.” Journal of Theoretical Biology, vol. 383, Elsevier, 2015, pp. 28–43, doi:10.1016/j.jtbi.2015.07.011.","ama":"Paixao T, Badkobeh G, Barton NH, et al. Toward a unifying framework for evolutionary processes. Journal of Theoretical Biology. 2015;383:28-43. doi:10.1016/j.jtbi.2015.07.011","short":"T. Paixao, G. Badkobeh, N.H. Barton, D. Çörüş, D. Dang, T. Friedrich, P. Lehre, D. Sudholt, A. Sutton, B. Trubenova, Journal of Theoretical Biology 383 (2015) 28–43.","ista":"Paixao T, Badkobeh G, Barton NH, Çörüş D, Dang D, Friedrich T, Lehre P, Sudholt D, Sutton A, Trubenova B. 2015. Toward a unifying framework for evolutionary processes. Journal of Theoretical Biology. 383, 28–43.","ieee":"T. Paixao et al., “Toward a unifying framework for evolutionary processes,” Journal of Theoretical Biology, vol. 383, pp. 28–43, 2015.","apa":"Paixao, T., Badkobeh, G., Barton, N. H., Çörüş, D., Dang, D., Friedrich, T., … Trubenova, B. (2015). Toward a unifying framework for evolutionary processes. Journal of Theoretical Biology, 383, 28–43. https://doi.org/10.1016/j.jtbi.2015.07.011"},"language":[{"iso":"eng"}],"accept":"1","day":"21","ddc":["570"],"title":"Toward a unifying framework for evolutionary processes","file":[{"relation":"main_file","date_updated":"2018-12-12T10:16:53Z","content_type":"application/pdf","file_id":"5244","creator":"system","access_level":"open_access","open_access":1,"date_created":"2018-12-12T10:16:53Z","file_name":"IST-2016-483-v1+1_1-s2.0-S0022519315003409-main.pdf","file_size":595307}],"year":"2015","month":"10","file_date_updated":"2018-12-12T10:16:53Z","abstract":[{"lang":"eng","text":"The theory of population genetics and evolutionary computation have been evolving separately for nearly 30 years. Many results have been independently obtained in both fields and many others are unique to its respective field. We aim to bridge this gap by developing a unifying framework for evolutionary processes that allows both evolutionary algorithms and population genetics models to be cast in the same formal framework. The framework we present here decomposes the evolutionary process into its several components in order to facilitate the identification of similarities between different models. In particular, we propose a classification of evolutionary operators based on the defining properties of the different components. We cast several commonly used operators from both fields into this common framework. Using this, we map different evolutionary and genetic algorithms to different evolutionary regimes and identify candidates with the most potential for the translation of results between the fields. This provides a unified description of evolutionary processes and represents a stepping stone towards new tools and results to both fields. "}],"status":"public","type":"journal_article","date_updated":"2019-08-02T12:37:14Z","quality_controlled":"1","author":[{"orcid":"0000-0003-2361-3953","last_name":"Paixao","full_name":"Paixao, Tiago","first_name":"Tiago","id":"2C5658E6-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Badkobeh","first_name":"Golnaz","full_name":"Badkobeh, Golnaz"},{"id":"4880FE40-F248-11E8-B48F-1D18A9856A87","full_name":"Barton, Nicholas H","first_name":"Nicholas H","orcid":"0000-0002-8548-5240","last_name":"Barton"},{"last_name":"Çörüş","full_name":"Çörüş, Doğan","first_name":"Doğan"},{"full_name":"Dang, Duccuong","first_name":"Duccuong","last_name":"Dang"},{"last_name":"Friedrich","full_name":"Friedrich, Tobias","first_name":"Tobias"},{"last_name":"Lehre","full_name":"Lehre, Per","first_name":"Per"},{"last_name":"Sudholt","full_name":"Sudholt, Dirk","first_name":"Dirk"},{"last_name":"Sutton","full_name":"Sutton, Andrew","first_name":"Andrew"},{"full_name":"Trubenova, Barbora","first_name":"Barbora","id":"42302D54-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-6873-2967","last_name":"Trubenova"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publist_id":"5629","oa":1,"cc_license":"cc_by_nc_nd","doi":"10.1016/j.jtbi.2015.07.011","pubrep_id":"483"},{"main_file_link":[{"url":"http://arxiv.org/abs/1504.06260","open_access":"1"}],"department":[{"_id":"NiBa"},{"_id":"CaGu"}],"conference":{"location":"Madrid, Spain","start_date":"2015-07-11","name":"GECCO: Genetic and evolutionary computation conference","end_date":"2015-07-15"},"date_created":"2018-12-11T11:51:58Z","month":"07","_id":"1430","publication":"Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation","year":"2015","title":"First steps towards a runtime comparison of natural and artificial evolution","language":[{"iso":"eng"}],"project":[{"grant_number":"618091","name":"Speed of Adaptation in Population Genetics and Evolutionary Computation","_id":"25B1EC9E-B435-11E9-9278-68D0E5697425"}],"citation":{"chicago":"Paixao, Tiago, Dirk Sudholt, Jorge Heredia, and Barbora Trubenova. “First Steps towards a Runtime Comparison of Natural and Artificial Evolution.” In Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, 1455–62. ACM, 2015. https://doi.org/10.1145/2739480.2754758.","ama":"Paixao T, Sudholt D, Heredia J, Trubenova B. First steps towards a runtime comparison of natural and artificial evolution. In: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation. ACM; 2015:1455-1462. doi:10.1145/2739480.2754758","mla":"Paixao, Tiago, et al. “First Steps towards a Runtime Comparison of Natural and Artificial Evolution.” Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, ACM, 2015, pp. 1455–62, doi:10.1145/2739480.2754758.","ieee":"T. Paixao, D. Sudholt, J. Heredia, and B. Trubenova, “First steps towards a runtime comparison of natural and artificial evolution,” in Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, Madrid, Spain, 2015, pp. 1455–1462.","ista":"Paixao T, Sudholt D, Heredia J, Trubenova B. 2015. First steps towards a runtime comparison of natural and artificial evolution. Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation. GECCO: Genetic and evolutionary computation conference 1455–1462.","short":"T. Paixao, D. Sudholt, J. Heredia, B. Trubenova, in:, Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, ACM, 2015, pp. 1455–1462.","apa":"Paixao, T., Sudholt, D., Heredia, J., & Trubenova, B. (2015). First steps towards a runtime comparison of natural and artificial evolution. In Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation (pp. 1455–1462). Madrid, Spain: ACM. https://doi.org/10.1145/2739480.2754758"},"day":"11","publisher":"ACM","page":"1455 - 1462","doi":"10.1145/2739480.2754758","publication_status":"published","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publist_id":"5768","date_published":"2015-07-11T00:00:00Z","oa":1,"quality_controlled":"1","date_updated":"2019-08-02T12:37:07Z","oa_version":"Preprint","author":[{"last_name":"Paixao","orcid":"0000-0003-2361-3953","id":"2C5658E6-F248-11E8-B48F-1D18A9856A87","first_name":"Tiago","full_name":"Paixao, Tiago"},{"last_name":"Sudholt","first_name":"Dirk","full_name":"Sudholt, Dirk"},{"last_name":"Heredia","full_name":"Heredia, Jorge","first_name":"Jorge"},{"full_name":"Trubenova, Barbora","first_name":"Barbora","id":"42302D54-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-6873-2967","last_name":"Trubenova"}],"abstract":[{"lang":"eng","text":"Evolutionary algorithms (EAs) form a popular optimisation paradigm inspired by natural evolution. In recent years the field of evolutionary computation has developed a rigorous analytical theory to analyse their runtime on many illustrative problems. Here we apply this theory to a simple model of natural evolution. In the Strong Selection Weak Mutation (SSWM) evolutionary regime the time between occurrence of new mutations is much longer than the time it takes for a new beneficial mutation to take over the population. In this situation, the population only contains copies of one genotype and evolution can be modelled as a (1+1)-type process where the probability of accepting a new genotype (improvements or worsenings) depends on the change in fitness. We present an initial runtime analysis of SSWM, quantifying its performance for various parameters and investigating differences to the (1+1) EA. We show that SSWM can have a moderate advantage over the (1+1) EA at crossing fitness valleys and study an example where SSWM outperforms the (1+1) EA by taking advantage of information on the fitness gradient."}],"status":"public","type":"conference"},{"department":[{"_id":"NiBa"}],"date_created":"2018-12-11T11:54:07Z","publisher":"Public Library of Science","_id":"1809","publication":"PLoS One","issue":"5","date_published":"2015-05-18T00:00:00Z","publication_status":"published","volume":10,"oa_version":"Published Version","month":"05","file_date_updated":"2018-12-12T10:09:07Z","intvolume":" 10","citation":{"ieee":"B. Trubenova, S. Novak, and R. Hager, “Indirect genetic effects and the dynamics of social interactions,” PLoS One, vol. 10, no. 5, 2015.","ista":"Trubenova B, Novak S, Hager R. 2015. Indirect genetic effects and the dynamics of social interactions. PLoS One. 10(5).","short":"B. Trubenova, S. Novak, R. Hager, PLoS One 10 (2015).","apa":"Trubenova, B., Novak, S., & Hager, R. (2015). Indirect genetic effects and the dynamics of social interactions. PLoS One, 10(5). https://doi.org/10.1371/journal.pone.0126907","chicago":"Trubenova, Barbora, Sebastian Novak, and Reinmar Hager. “Indirect Genetic Effects and the Dynamics of Social Interactions.” PLoS One 10, no. 5 (2015). https://doi.org/10.1371/journal.pone.0126907.","ama":"Trubenova B, Novak S, Hager R. Indirect genetic effects and the dynamics of social interactions. PLoS One. 2015;10(5). doi:10.1371/journal.pone.0126907","mla":"Trubenova, Barbora, et al. “Indirect Genetic Effects and the Dynamics of Social Interactions.” PLoS One, vol. 10, no. 5, Public Library of Science, 2015, doi:10.1371/journal.pone.0126907."},"language":[{"iso":"eng"}],"accept":"1","day":"18","ddc":["570","576"],"file":[{"file_name":"IST-2016-453-v1+1_journal.pone.0126907.pdf","open_access":1,"date_created":"2018-12-12T10:09:07Z","file_size":2748982,"relation":"main_file","content_type":"application/pdf","file_id":"4730","date_updated":"2018-12-12T10:09:07Z","creator":"system","access_level":"open_access"}],"title":"Indirect genetic effects and the dynamics of social interactions","year":"2015","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa":1,"publist_id":"5299","cc_license":"'https://creativecommons.org/licenses/by/4.0/'","doi":"10.1371/journal.pone.0126907","pubrep_id":"453","abstract":[{"text":"Background: Indirect genetic effects (IGEs) occur when genes expressed in one individual alter the expression of traits in social partners. Previous studies focused on the evolutionary consequences and evolutionary dynamics of IGEs, using equilibrium solutions to predict phenotypes in subsequent generations. However, whether or not such steady states may be reached may depend on the dynamics of interactions themselves. Results: In our study, we focus on the dynamics of social interactions and indirect genetic effects and investigate how they modify phenotypes over time. Unlike previous IGE studies, we do not analyse evolutionary dynamics; rather we consider within-individual phenotypic changes, also referred to as phenotypic plasticity. We analyse iterative interactions, when individuals interact in a series of discontinuous events, and investigate the stability of steady state solutions and the dependence on model parameters, such as population size, strength, and the nature of interactions. We show that for interactions where a feedback loop occurs, the possible parameter space of interaction strength is fairly limited, affecting the evolutionary consequences of IGEs. We discuss the implications of our results for current IGE model predictions and their limitations.","lang":"eng"}],"status":"public","type":"journal_article","date_updated":"2019-08-02T12:37:27Z","quality_controlled":"1","author":[{"orcid":"0000-0002-6873-2967","last_name":"Trubenova","full_name":"Trubenova, Barbora","first_name":"Barbora","id":"42302D54-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Novak","full_name":"Novak, Sebastian","first_name":"Sebastian","id":"461468AE-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Hager","full_name":"Hager, Reinmar","first_name":"Reinmar"}]}]